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Les Arènes (2018), 340 pages
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"A former Wall Street quantitative analyst sounds an alarm on mathematical modeling, a pervasive new force in society that threatens to undermine democracy and widen inequality,"--NoveList. "We live in the age of the algorithm. Increasingly, the decisions that affect our lives-- where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination: If a poor student can't get a loan because a lending model deems him too risky (by virtue of his zip code), he's then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a 'toxic cocktail for democracy.' Welcome to the dark side of Big Data. Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These 'weapons of math destruction' score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. O'Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it's up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change."--Dust jacket.… (more)
User reviews
LibraryThing member DavidWineberg
Putting you in your place
We model everything now. Teacher evaluations, job applicants, credit applications, online purchasing, voting patterns, crime – pretty much anything you can think of is modeled in some opaque black box of unaccountable algorithms. They are so inherently faulty,
Something as simple as a zip code can tell a system what kind of neighborhood you live in, and make assumptions. Search history, social media activity, purchase record – all contribute to an instant decision that you are worthy or not. These values are plugged in to school applications, job applications, and personal evaluations such as HR records, personality tests and even dating sites. Even purged, forgiven, and expired details remain active. Police model neighborhoods. They harass residents for every little thing in poorer neighborhoods, while giving a free pass to wealthier ones, where crimes are far bigger, but mostly white collar. Only ten states have outlawed the use of credit checks on job applications. For shopping at downscale stores, credit cardholders had their limits slashed, making them poorer and making them poorer risks – as in higher interest rates. It is computer models that schedule shifts, without concern for the needs of the employee in terms of child care, time off between shifts, or advance notice. Managers are paid to optimize revenue per hour worked, so memos from above go unheeded.
That models are often incorrect, badly designed, misinformed and misconstrued, means that people are denied service, or not hired, or outright fired. But there’s always someone else behind them, so it’s just the cost of doing business. “Unfairness is the black stuff belching out of the smokestacks. It’s an emission, a toxic one,” O’Neil says. We are all just collateral damage.
One insurance company instantly evaluates whether a customer is likely to shop around. If it judges not, it charges them more. It actually has 100,000 microsegments (buckets) depending on instant customer scores. In Florida, a driver with a clean record but a poor credit score pays $1552 more for insurance than a driver with a high credit score and a drunk driving conviction. Shopping sites won’t offer you a discount if you are already logged in. Payday loans and for profit schools prey on the disadvantaged and the desperate, extracting billions from them. The games are endless.
WMD is extremely fast paced, fact packed, and depressing. It has come to the point that machines dictate who may have a successful life, right out of the gate. Initiative, courage, creativity, drive, human kindness – don’t enter into it. We are all typecast by Big Data - assigned values mathematically that can stymie a life. There is no appeal. There isn’t even any knowing. The poor get poorer. The rich find the new era refreshing.
And of course, none of this is transparent. Customers cannot arrive at these prices, these decisions or these scores themselves. It’s all in the math, manipulating us. And yet, 73% of Americans believe search engine results are “accurate and impartial”. 62% believe Facebook posts their submissions to everyone. Nothing could be farther from the truth. Worse, data banks draw on each other, multiplying their errors, sometimes creating completely false profiles of a person, who then cannot get a job, rent an apartment or buy a car.
O’Neil says she is outraged by her own profession. You will be too.
David Wineberg
We model everything now. Teacher evaluations, job applicants, credit applications, online purchasing, voting patterns, crime – pretty much anything you can think of is modeled in some opaque black box of unaccountable algorithms. They are so inherently faulty,
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discriminatory and racist as to be shameful. They cut off the poor from keeping up, and provide the wealthy with all kinds of advantages. Data Scientist Cathy O’Neil says in Weapons of Math Destruction: ”Big Data processes codify the past. They do not invent the future.”Something as simple as a zip code can tell a system what kind of neighborhood you live in, and make assumptions. Search history, social media activity, purchase record – all contribute to an instant decision that you are worthy or not. These values are plugged in to school applications, job applications, and personal evaluations such as HR records, personality tests and even dating sites. Even purged, forgiven, and expired details remain active. Police model neighborhoods. They harass residents for every little thing in poorer neighborhoods, while giving a free pass to wealthier ones, where crimes are far bigger, but mostly white collar. Only ten states have outlawed the use of credit checks on job applications. For shopping at downscale stores, credit cardholders had their limits slashed, making them poorer and making them poorer risks – as in higher interest rates. It is computer models that schedule shifts, without concern for the needs of the employee in terms of child care, time off between shifts, or advance notice. Managers are paid to optimize revenue per hour worked, so memos from above go unheeded.
That models are often incorrect, badly designed, misinformed and misconstrued, means that people are denied service, or not hired, or outright fired. But there’s always someone else behind them, so it’s just the cost of doing business. “Unfairness is the black stuff belching out of the smokestacks. It’s an emission, a toxic one,” O’Neil says. We are all just collateral damage.
One insurance company instantly evaluates whether a customer is likely to shop around. If it judges not, it charges them more. It actually has 100,000 microsegments (buckets) depending on instant customer scores. In Florida, a driver with a clean record but a poor credit score pays $1552 more for insurance than a driver with a high credit score and a drunk driving conviction. Shopping sites won’t offer you a discount if you are already logged in. Payday loans and for profit schools prey on the disadvantaged and the desperate, extracting billions from them. The games are endless.
WMD is extremely fast paced, fact packed, and depressing. It has come to the point that machines dictate who may have a successful life, right out of the gate. Initiative, courage, creativity, drive, human kindness – don’t enter into it. We are all typecast by Big Data - assigned values mathematically that can stymie a life. There is no appeal. There isn’t even any knowing. The poor get poorer. The rich find the new era refreshing.
And of course, none of this is transparent. Customers cannot arrive at these prices, these decisions or these scores themselves. It’s all in the math, manipulating us. And yet, 73% of Americans believe search engine results are “accurate and impartial”. 62% believe Facebook posts their submissions to everyone. Nothing could be farther from the truth. Worse, data banks draw on each other, multiplying their errors, sometimes creating completely false profiles of a person, who then cannot get a job, rent an apartment or buy a car.
O’Neil says she is outraged by her own profession. You will be too.
David Wineberg
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LibraryThing member nbmars
I thought I knew a lot about Big Data, but this book opened my eyes to the many hidden ways in which the uses to which it has been put are much more far-reaching and harmful than I had imagined.
In particular, as the author points out in many convincing ways, applications of Big Data “punish the
The author calls the mathematical models employing Big Data and used to such harmful effect “Weapons of Math Destruction” or WMDs.
In WMDs, she explains, “poisonous assumptions . . . camouflaged by math go largely untested and unquestioned.” They create their own toxic feedback loops, and, to an extent which shocked me, guide decisions in a large variety of areas from advertising to prisons to healthcare to hiring and firing decisions. Most importantly, because they rely on esoteric mathematical models (no matter that they are many times based on toxic, biased, and/or erroneous assumptions):
“They’re opaque, unquestioned, and unaccountable, and they operate at a scale to sort, target, or ‘optimize’ millions of people.”
The goal is always profit, but what is lost is fairness, the recognition of individual exceptions, and simple compassion and humanity. The author demonstrates conclusively how the uses to which Big Data are put adds to the growing dystopia and inequality gap.
I am not at all versed in math, but the author manages to explain how all this works without requiring that one understand specific algorithms. She provides specific examples from the worlds of teacher evaluations, hiring decisions generally, advertising, insurance, policing, college admissions, lending and credit evaluation, and political targeting.
One of the saddest chapters (and they are all sad, unfortunately) is about the many for-profit universities (Trump University comes to mind) that specifically target people in great need, selling them overpriced promises of success. Her quotes from the marketing materials of these places are horrifying. They look for individuals who are “isolated,” with “low self esteem” who have “few people in their lives who care about them” and feel “stuck.” She shows how they use google searches, residential data, and Facebook posts, inter alia, to find “the most desperate among us at enormous scale”:
“In education, they promise what’s usually a false road to prosperity, while also calculating how to maximize the dollars they draw from each prospect. Their operations cause immense and nefarious feedback loops and leave their customers buried under mountains of debt.”
The chapter on the way the “stop and frisk” policing operates is also very depressing; and in truth we have seen the tragic results in city after city.
The fact is, the whole book is rather a downer, albeit an important one. Although O’Neil cites a few programs that have used Big Data to help people rather than to enrich a few and oppress the rest, can one really think that “moral imagination” can take precedence over prejudice and greed? Personally, I’m not so sure. The author provides ideas about how to change (and importantly, regulate) uses of Big Data, but she is more optimistic than I am, ending on a positive note:
“We must come together to police these WMDs, to tame and disarm them. My hope is that they’ll be remembered, like the deadly coal mines of a century ago, as relics of the early days of this new revolution, before we learned how to bring fairness and accountability to the age of data. Math deserves much better than WMDs, and democracy does too.”
Evaluation: I hope this important book gets a lot of attention. My husband always makes the argument about privacy concerns that what do we care if we’ve done nothing wrong? This book shows how, astoundingly, that isn’t enough to stop Big Data from hurting us in many aspects of our lives. It is a critical lesson for today’s world, and the world of our children.
In particular, as the author points out in many convincing ways, applications of Big Data “punish the
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poor and the oppressed in our society, while making the rich richer.” She paints a sobering picture.The author calls the mathematical models employing Big Data and used to such harmful effect “Weapons of Math Destruction” or WMDs.
In WMDs, she explains, “poisonous assumptions . . . camouflaged by math go largely untested and unquestioned.” They create their own toxic feedback loops, and, to an extent which shocked me, guide decisions in a large variety of areas from advertising to prisons to healthcare to hiring and firing decisions. Most importantly, because they rely on esoteric mathematical models (no matter that they are many times based on toxic, biased, and/or erroneous assumptions):
“They’re opaque, unquestioned, and unaccountable, and they operate at a scale to sort, target, or ‘optimize’ millions of people.”
The goal is always profit, but what is lost is fairness, the recognition of individual exceptions, and simple compassion and humanity. The author demonstrates conclusively how the uses to which Big Data are put adds to the growing dystopia and inequality gap.
I am not at all versed in math, but the author manages to explain how all this works without requiring that one understand specific algorithms. She provides specific examples from the worlds of teacher evaluations, hiring decisions generally, advertising, insurance, policing, college admissions, lending and credit evaluation, and political targeting.
One of the saddest chapters (and they are all sad, unfortunately) is about the many for-profit universities (Trump University comes to mind) that specifically target people in great need, selling them overpriced promises of success. Her quotes from the marketing materials of these places are horrifying. They look for individuals who are “isolated,” with “low self esteem” who have “few people in their lives who care about them” and feel “stuck.” She shows how they use google searches, residential data, and Facebook posts, inter alia, to find “the most desperate among us at enormous scale”:
“In education, they promise what’s usually a false road to prosperity, while also calculating how to maximize the dollars they draw from each prospect. Their operations cause immense and nefarious feedback loops and leave their customers buried under mountains of debt.”
The chapter on the way the “stop and frisk” policing operates is also very depressing; and in truth we have seen the tragic results in city after city.
The fact is, the whole book is rather a downer, albeit an important one. Although O’Neil cites a few programs that have used Big Data to help people rather than to enrich a few and oppress the rest, can one really think that “moral imagination” can take precedence over prejudice and greed? Personally, I’m not so sure. The author provides ideas about how to change (and importantly, regulate) uses of Big Data, but she is more optimistic than I am, ending on a positive note:
“We must come together to police these WMDs, to tame and disarm them. My hope is that they’ll be remembered, like the deadly coal mines of a century ago, as relics of the early days of this new revolution, before we learned how to bring fairness and accountability to the age of data. Math deserves much better than WMDs, and democracy does too.”
Evaluation: I hope this important book gets a lot of attention. My husband always makes the argument about privacy concerns that what do we care if we’ve done nothing wrong? This book shows how, astoundingly, that isn’t enough to stop Big Data from hurting us in many aspects of our lives. It is a critical lesson for today’s world, and the world of our children.
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LibraryThing member DoctorDebt
Cathy O’Neill’s Weapons of Math Destruction is an approachable, easy to digest book on a big, complex subject: big data and how it affects our everyday lives. It covers several areas people may be familiar with in passing, but probably haven’t thought much about (college rankings, for
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example), and how algorithms and big data come into play with each of them – not necessarily to our benefit. The second to last chapter – dealing with algorithms undermining democracy – is particularly timely, given the last election cycle. Recommended reading for those interested in business information systems, big data, and ethics in the age of technology. Show Less
LibraryThing member mmparker
I'm not really the audience for this book, since I work in the field and have been reading about the awful misapplication of algorithms for years. The tone is a little too pop-science-gee-whiz for my taste, but O'Neil does a thorough job of exploring the many ways in which the methods of the field
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can be abused. Show Less
LibraryThing member dogboi
A WMD, Weapon of Math Destruction, is an algorithm that is a block box (opaque), used at scale, and damages the lives of people, generally poor minorities. Cathy O'Neil goes through a lot of detail describing several of these WMDs and how they are ruining people's lives. Hate Clopening? (working at
This book is important, and I think it should be read by anyone concerned about how Big Data can be used to harm us all. As someone whose future career depends upon algorithmic learning, statistics, and mathematics, I can say this book was eye opening. I'm used to hearing about the power of algorithms and modeling, but really, a model is not the thing that it models (as every mathematician knows).
This book is a lot more accessible than Derman's Models.Behaving.Badly, even if it is in the same vein. It has a much clearer focus, and it very clearly explains the traps mathematical modeling has created. I highly recommend this book to everyone. It doesn't require an understanding of math (there are no models or equations in this book). Just an understanding of how algorithms can contain bias through the use of proxies. Read it and share it.
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Closing and then Opening up the next morning). It's likely an algorithm created that schedule. Hate the fact that employers now use opaque personality tests to look for mental illness while you're applying for a job? Another WMD. This book is important, and I think it should be read by anyone concerned about how Big Data can be used to harm us all. As someone whose future career depends upon algorithmic learning, statistics, and mathematics, I can say this book was eye opening. I'm used to hearing about the power of algorithms and modeling, but really, a model is not the thing that it models (as every mathematician knows).
This book is a lot more accessible than Derman's Models.Behaving.Badly, even if it is in the same vein. It has a much clearer focus, and it very clearly explains the traps mathematical modeling has created. I highly recommend this book to everyone. It doesn't require an understanding of math (there are no models or equations in this book). Just an understanding of how algorithms can contain bias through the use of proxies. Read it and share it.
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LibraryThing member rivkat
O’Neil is a popular blogger who writes about the risks of big data applied to populations seeking credit, being evaluated for parole, seeking jobs, trying to get into college, being evaluated for effectiveness as teachers, and so on. The biggest takeaway from this short book, which is well worth
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reading, is that big data’s often fatal flaw is lack of feedback. Really effective models assist their users only if they neither over-include nor under-exclude, like Moneyball or 538’s models or even Google’s search engine. Which means that you follow up on people you predicted would succeed and people you predicted would fail, and if you missed their performance you try to update the model. But algorithms that deny people credit neither follow up on them nor leave their subsequent performance unaffected by the operation of the model—a person who can only get credit at 18% a month will predictably be more likely to default than someone who got 2%. Likewise, teacher evaluation algorithms don’t have independent measures to cross-check; a teacher fired for being ineffective doesn’t get fed back into the system if she leaves for another school system that doesn’t use the same metrics and then wins Teacher of the Year. Moreover, current models often take as predictors things that correlate with being poor and nonwhite, rather than treating those disparities as problems that need to be ameliorated by social and public policy. Show Less
LibraryThing member JosephKing6602
I really wanted to like this book; and while it was an easy & pleasant read, I was hoping that the author would go into a bit more depth regarding the actual types of 'math' that BIG data uses. What calculations are used to determine patterns? How are trends discovered?...What kind of powerful
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statistical procedures are used in analyzing the sea of data that is produced by the internet data pools? The examples were real and thought-provoking. I believed her warning about the danger of these tools for our democracy. However, I felt the author's theme in this book was scatter-shot and needed to be more focused on the specialty of data science. But overall, I am glad that I read it. #bigdata Show Less
LibraryThing member Stbalbach
Excellent repudiation of the prevailing ideology/religion of dataism. Its limitations and dangers. The stories presented here are not anecdotal or edge-case, they are central and impact nearly all of us. Dataism is seductive and the fault is usually not with the mathematicians or programmers, but
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management who wields them in ways they don't understand. The solution is to have a balance of algorithm and humans, with semi-automated systems helping decision makers. This is already happening with some robo-trader products that are a mix of algo and human. Regardless this issue will continue to grow and likely lead to a backlash at some point in the future as we continue to work out how to integrate computers safely into society. Show Less
LibraryThing member Evolvr
Cathy O'Neil's book is original and timely. No other book that I know of addresses the topic of the application of algorithms to our daily lives, and this topic will rapidly become more important as private and public entities at all levels utilize algorithms with increasing frequency to make
Given her deep and varied background in data science, O'Neil has the expertise to address the algorithms at their constructive level, however, the reader may be left a bit wanting for a deeper explanation of exactly how the algorithms in question is flawed. I think readers will find this either relieving or frustrating. As a scientist, there were times when I wanted a deeper explanation of the nature of a particular algorithm's flaw, but this is a book of social commentary at its heart, and many readers will welcome the absence of the particulars. Due in part to O"Neil's clear writing, you do not need any mathematical background to read and understand the book. Essentially, O'Neil makes the argument that people are not pieces of data, and if they are treated as such (as they will increasingly be), then there will be serious human consequences. While I don't agree with every argument, it is an important book and one which I am glad to have read, and I recommend it.
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decisions with little or no human input. Algorithms are mathematical models that apply a set of rules to data in order to help classify or make decisions about the data set. Taking decisions out of people's hands is ostensibly a good thing under many circumstances where prejudice has historically impeded advancement of specific groups within the American landscape. However O'Neil deftly describes some fundamental problems with the applied vs. theoretical algorithms. I would summarize this as a problem with scale, refinement, and suitability. O'Neil explains how algorithms can be used at the wrong scale, have ho feedback of outcomes in order to refine them, and/or are applied to data for which they were never designed to function. She does this through a series of anecdotes where the use of algorithms has had serious negative impacts on individuals or groups, and she puts a human face on the outliers - the people who have suffer when algorithms fail, when they always do for some portion of the data.Given her deep and varied background in data science, O'Neil has the expertise to address the algorithms at their constructive level, however, the reader may be left a bit wanting for a deeper explanation of exactly how the algorithms in question is flawed. I think readers will find this either relieving or frustrating. As a scientist, there were times when I wanted a deeper explanation of the nature of a particular algorithm's flaw, but this is a book of social commentary at its heart, and many readers will welcome the absence of the particulars. Due in part to O"Neil's clear writing, you do not need any mathematical background to read and understand the book. Essentially, O'Neil makes the argument that people are not pieces of data, and if they are treated as such (as they will increasingly be), then there will be serious human consequences. While I don't agree with every argument, it is an important book and one which I am glad to have read, and I recommend it.
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LibraryThing member pwagner2
A very important read. Eye opening!! I knew I was affected by some of this, but seeing how the war on the poor is reinforced by WMD's really opened my eyes.
Highly recommended!
Highly recommended!
LibraryThing member happysadnick
What happens when we place value in statistics that aren't always accurate? What happens when a group of people blindly accept these untested and opaque models as the foundation for their society? In search for a utopian society, Big Data promises to deliver us to a dystonian society where
"Data over people" is the message of this book, the author puts forth many examples of how we've truly lost touch with reality. Even though these data models are known to be wrong, there is no feedback loop or agenda to fix them. We accept everything and question nothing, as a result our future looks bleak and terrible. Individuals that are out-of-touch with the world will be dictating the path the world goes.
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everything is for sale and we can expect as many false positives as positives. The consequences of this data economy are huge, it is responsible for many negative factors in our society such as inequality and the loss of our civil liberties. Man is stripped of his human nature and reduced to a scientific animal with human behaviorism, he is only capable and the extent of his environment. "Data over people" is the message of this book, the author puts forth many examples of how we've truly lost touch with reality. Even though these data models are known to be wrong, there is no feedback loop or agenda to fix them. We accept everything and question nothing, as a result our future looks bleak and terrible. Individuals that are out-of-touch with the world will be dictating the path the world goes.
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LibraryThing member librarianwords
Although this book focuses on math, models and algorithms, Cathy O'Neill manages to bypass all of the technical jargon and explain how these models work in plain English. O'Neill's key point are that many of these models, such as evaluating teacher performance by using test scores, software used to
O'Neill spends most of the book going into detail about many different models and their unfairness. Part of the problems is that in our society's effort to create models to predict behavior, we use proxies so that we can manipulate the numbers, but proxies can never represent the real thing because life is complicated and cannot be boiled down to a set of numbers. Often these proxies help misrepresent the truth and in the case of most of these models, the ones who suffer are often those who are poor because the data that can be gathered on them doesn't read well: credit scores, zip codes, education, etc. None of these data points however, can tell you if they're trying to put their life back together but circumstances have been cruel, and so a large population of underserved people are now underserved by software. As she says on page 204, "Big Data processes codify the past. They do not invent the future." When a algorithm reinforces the past, it reinforces all of the things that come along with it, including the racism and, the classism and spits that back out.
O'Neill only stops to tell us how to fix this problem in the conclusion. After promising for the whole book to talk about how to fix this problem, it's only in the last twenty pages that she gets down to her proposed solutions. While her solutions seem good, they didn't seem as well researched as the rest of the book, and I felt rushed between one solution to the next. The only solution that seemed like it might actually work was the one that the EU implemented, only because it had actually been done before. If O'Neill had spent as much time researching and writing about her solutions as she did on even a fourth of the book, it would be a much better book, but instead it feels a little one-sided, without much of an idea on how to change this and move forward, much like some of the models she discusses.
This review was written for LibraryThing Early Reviewers.
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determine which resumes to look at for a job, and models designed to predict where crime will happen are unfair. She uses that term a lot, but she makes a compelling argument to back it up. Her main points are that certain models (but not all) have the ability to devastate and discriminate against a whole section of people, ad are currently doing so. The models that do this she calls Weapons of Math Destruction or WMDs. WMDs have three key components that make them dangerous, and without any one of them, they wouldn't be. These three characteristics are: Opacity on how the models makes determinations/what factors are included, Scale (only software that can affect a large number of people are WMDs), and lastly, these models have no feedback loop for errors. To clarify that last point, these models don't know when they've got it wrong. There is no way for it to know if a teacher it recommended get fired was actually a good teacher that eventually became principal at his or her new school and improved student success. There is no way for it to know if a person whose resume they weeded out would have taken the company into new directions. As she says on page 133, "The system has no inkling that it got one person, or even a thousand people entirely wrong." There simply isn't a way for the model to know that it's gotten it wrong, and rethink things. O'Neill spends most of the book going into detail about many different models and their unfairness. Part of the problems is that in our society's effort to create models to predict behavior, we use proxies so that we can manipulate the numbers, but proxies can never represent the real thing because life is complicated and cannot be boiled down to a set of numbers. Often these proxies help misrepresent the truth and in the case of most of these models, the ones who suffer are often those who are poor because the data that can be gathered on them doesn't read well: credit scores, zip codes, education, etc. None of these data points however, can tell you if they're trying to put their life back together but circumstances have been cruel, and so a large population of underserved people are now underserved by software. As she says on page 204, "Big Data processes codify the past. They do not invent the future." When a algorithm reinforces the past, it reinforces all of the things that come along with it, including the racism and, the classism and spits that back out.
O'Neill only stops to tell us how to fix this problem in the conclusion. After promising for the whole book to talk about how to fix this problem, it's only in the last twenty pages that she gets down to her proposed solutions. While her solutions seem good, they didn't seem as well researched as the rest of the book, and I felt rushed between one solution to the next. The only solution that seemed like it might actually work was the one that the EU implemented, only because it had actually been done before. If O'Neill had spent as much time researching and writing about her solutions as she did on even a fourth of the book, it would be a much better book, but instead it feels a little one-sided, without much of an idea on how to change this and move forward, much like some of the models she discusses.
This review was written for LibraryThing Early Reviewers.
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LibraryThing member dele2451
Finding a book authored by an Ivy League mathematician that is both interesting and relatable to non-mathematicians is always a nice surprise. The information O'Neil provides on often-unregulated data brokering and predictive software programs is fresh, crucial, and backed up by many examples of
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actual and potential problems (as well as assorted proposed solutions) for American consumers and business leaders of all types. Even if number crunching isn't your bag, I recommend reading it to gain awareness of new computational technology trends that directly impact your wallet--and your privacy--in invisible ways. Should definitely be on educator/criminal justice/finance major reading lists. I'd probably have rated it higher, but she lost a couple notches with me for obvious lapses in political impartiality in a book devoted to quantitative mathematical science. Show Less
LibraryThing member pbirch01
This cleverly-titled book seems to have been a direct response to the Occupy movement and is a very focused and well-argued overview of some of the decision algorithms that influence all aspects of society. Over the ten chapters of the book, O'Neil introduces us to different algorithms in ten areas
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of modern society and then slowly goes through how they were implemented and how they can be biased against certain groups. O'Neil does an excellent job translating the math into very understandable examples and walking the reader through how so many of these algorithms are used in different areas of modern society. The examples do seem slightly repetitive but her focus on pulling the cover back never wavers and provides something tangible for the reader to be frustrated about. Even though I thought I know about many of these algorithms, I definitely thought more about this issue such as why do I have to provide my SSN number to sign up for a cell phone. Hopefully with more people reading this we will be able to demand more transparency in these algorithms which could lead to a more just society. Show Less
LibraryThing member cmc
A nice overview of the misuses and inherent biases of many different decision-making tools based on "big data". O'Neil dubs these biased algorithms "Weapons of Math Destruction", and provides many examples of the negative effects of these tools, in particular, of the disenfranchisement, social
She calls for careful ethical consideration and assessment of these toxic algorithms, but I was left feeling that the political will to regulate these tools is unlikely to be there for us, especially as these tools are exceptionally helpful for businesses to maximize their profits at the expense of employees and customers alike. Add in her observations that many of the worst offenders either actively help the richest Americans or can be avoided by the application of a bit of money, and it's hard to see any hope of change until such time as some sufficiently horrible event occurs that affectis a broad enough range of people to force change. (Just kidding, obviously, as the 2008 financial crisis would have seemed like just the sort of thing to make the government punish offenders and regulate the financial industry, but that certainly didn't happen.)
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immobility, credit traps, price gouging, and other nastiness done to poor people in the name of efficiency and profit. Luckily for the poor, these algorithms are also increasingly affecting middle class people in similar ways, as companies insinuate they way into people's driving habits, recreational choices, "wellness", psychological state, and political relevance.She calls for careful ethical consideration and assessment of these toxic algorithms, but I was left feeling that the political will to regulate these tools is unlikely to be there for us, especially as these tools are exceptionally helpful for businesses to maximize their profits at the expense of employees and customers alike. Add in her observations that many of the worst offenders either actively help the richest Americans or can be avoided by the application of a bit of money, and it's hard to see any hope of change until such time as some sufficiently horrible event occurs that affectis a broad enough range of people to force change. (Just kidding, obviously, as the 2008 financial crisis would have seemed like just the sort of thing to make the government punish offenders and regulate the financial industry, but that certainly didn't happen.)
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LibraryThing member Micah
Weapons of Math Destruction does a fine job showing how big data compounds and complicates bias against the poor. From credit cards to recidivism, the deck seems stacked against social mobility.
LibraryThing member weeta
upsetting, not surprising.
LibraryThing member calmclam
I thought this was a very interesting discussion of the risks of AI algorithms. In particular, the discussions about transparency and accountability are very interesting and disturbing.
LibraryThing member sullijo
Cathy O'Neil's Weapons of Math Destruction explores how the use of data mining and "neutral" algorithms wind up having a much larger impact on our lives than we might suspect. O'Neil covers a variety of subjects, including employment, advertising, political engagement, and consumer credit,
As much commentary as explanation, O'Neil is particularly interested in how these algorithms create and sustain feedback loops which perpetuate the very stereotypes and discriminatory practices they were meant to alleviate.
Weapons of Math Destruction does not require an understanding of advanced math, and O'Neil does a good job of explaining the underlying principals without relying on jargon. I would recommend it to anyone interested in how technological systems are playing an increasing -- and invisible role -- in shaping our society.
N.B.: I received a free copy of this book from LibraryThing's Early Reviewer program.
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demonstrating how businesses use complex mathematical systems to pad their bottom line without addressing real and pervasive discrimination.As much commentary as explanation, O'Neil is particularly interested in how these algorithms create and sustain feedback loops which perpetuate the very stereotypes and discriminatory practices they were meant to alleviate.
Weapons of Math Destruction does not require an understanding of advanced math, and O'Neil does a good job of explaining the underlying principals without relying on jargon. I would recommend it to anyone interested in how technological systems are playing an increasing -- and invisible role -- in shaping our society.
N.B.: I received a free copy of this book from LibraryThing's Early Reviewer program.
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LibraryThing member larg98
Loved it! Very clear explanation of the mathematical models that govern our lives. It shows us how unfair actual systems can be.
LibraryThing member AmandaWelling
This book just wasn't for me, I think. I didn't finish it and it will be passed along to someone who can appreciate it more. I gave up at the halfway point. I liked the subject, just didn't like the writing style!
LibraryThing member jamesgwld
Very timely book. Cathy, once a data analyst for D.E. Shaw, illuminates the inhumane direction of humanity through algorithms. These algorithms separate class and race amongst other things. Education, auto insurance, Facebook is all under scrutiny for their manipulation of the unaware public.
LibraryThing member Nanerz
O'Neil takes us on a journey through life, and explains how big data, although useful in many capacities, can be detrimental to the lives of many people such as low income, minorities, and the mentally ill. A main take away is that for big data models to be successful there needs to be continuous
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"smart" evolution of models using data gathered overtime, and an acknowledgment that there can be harm done to some segments of the population. This was well worth the read, and something I would suggest to those interested in the implications of big data on society. Show Less
LibraryThing member Cataloger623
Weapons of Math Destruction, How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil. The over the top subtitle fairly well summarizes what the premise of the book is. WMD’s are mathematical models or algorithms that are used predict human behavior, and aide in decision
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making. In benign situations these models determine who you are matched with on dating sites, what movies Netflix's suggest you see, the products Amazon offers you and things of that ilk. In less than benign situations these models come up with the probabilities that suggest which person maybe a bad hire, a risky borrower, or a potential terrorist. These models determine if you receive advertisements from a payday loan service, a mutual fund company or determine how you are sentenced in criminal cases. O’Neil argues that the continuous use of these programs can create loops that keep the poor, poor and allow the rich to get richer. These models lack transparency, the results tend to unquestioned, and the creators are unaccountable for their actions. O’Neil is unapologetically biased against the widespread use of big data and WMD’s. The book focuses on the damage done by these models and social injustice they wreak on the populous. O’Neil’s does a good job in support of her premise. She uses over 200 citations in addition to extensive footnotes and the book has a good index. O’Neil is highly qualified author in this field. She has a Ph.D in mathematics from Harvard and is considered an authority in the field. She is a good writer, although each chapter follows a similar pattern. Each chapter covers a specific area of concern covering topics from college admissions, hiring practices, loan approval, and online advertising. She then demonstrates the harmful effects of big data and WMD’s. Politically she believes, “Successful micro-targeting, in part, explains why in 2015 more than 43 percent of Republicans ---still believe the that President Obama is a Muslin and that 20 percent believe he was born outside the United States”. Her solution to this issue involves more government oversight, greater transparency in the industry, and European style individual control of their data. Show Less
LibraryThing member vonze
I am not a math person. Never have been, never will be. However, after learning about this book from the 2016 Goodreads Choice Awards, I thought it'd be a good gift for my husband, who is a math nerd.
For me, the book is reminiscent of Freaknonomics. It covers thought-provoking, seemingly hidden,
Glad someone is highlighting these important issues, for the economy, for math, for science, and for people's livelihoods.
I received this book from the Blogging for Books program in exchange for this review.
For me, the book is reminiscent of Freaknonomics. It covers thought-provoking, seemingly hidden,
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forces that rule our society. I really related to her example of a college student who struggled to get a minimum wage job to stay out of debt, because of interview personality questions that are looking for "one type" of worker. Clearly, regardless of dedication, need, or references. I faced a similar situation during the recession.Glad someone is highlighting these important issues, for the economy, for math, for science, and for people's livelihoods.
I received this book from the Blogging for Books program in exchange for this review.
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Awards
National Book Award (Longlist — Nonfiction — 2016)
Euler Book Prize (2019)
Boston Globe Best Book (Nonfiction — 2016)
The New York Times Notable Books of the Year (Nonfiction — 2016)
Kirkus Reviews Best Book of the Year (Science and Technology — 2016)
Chicago Public Library Best of the Best: Adults (Selection — 2016)
Collection
DDC/MDS
| 005.7 |
Original publication date
2016-09-06
Physical description
340 p.; 5.59 inches
ISBN
2352049806 / 9782352049807